OCRNet (Object Contextual Representation Network) is a deep learning model for semantic segmentation that enhances pixel-level understanding by incorporating object context information. It learns object regions from ground-truth segmentation and aggregates pixel representations within these regions. By computing relationships between pixels and object regions, OCRNet augments each pixel's representation with contextual information from relevant objects. This approach improves segmentation accuracy, particularly in complex scenes, by better capturing object boundaries and contextual relationships between different image elements.
| GPU | IXUCA SDK | Release |
|---|---|---|
| BI-V100 | 2.2.0 | 22.09 |
Go to visit Cityscapes official website, then choose 'Download' to download the Cityscapes dataset.
Specify /path/to/cityscapes to your Cityscapes path in later training process, the unzipped dataset path structure
sholud look like:
cityscapes/
├── gtFine
│ ├── test
│ ├── train
│ │ ├── aachen
│ │ └── bochum
│ └── val
│ ├── frankfurt
│ ├── lindau
│ └── munster
└── leftImg8bit
├── train
│ ├── aachen
│ └── bochum
└── val
├── frankfurt
├── lindau
└── munstermkdir data/
ln -s /path/to/cityscapes data/cityscapesbash init.sh# Train using 1x GPU card
bash train.sh
# Train using 4x GPU cards
bash train_distx4.sh
# Training using 8 x GPU
bash train_distx8.sh